Cloudera Databricks Data Science Certification Questions and Answers (Dumps and Practice Questions)

Question : Which of the following are advantages of the Support Vector Machines?
1. Effective in high dimensional spaces.
2. it is memory efficient
3. possible to specify custom kernels
4. Effective in cases where number of dimensions is greater than the number of samples
5. Number of features is much greater than the number of samples, the method still give good performances
6. SVMs directly provide probability estimates


1. 1,2,3,4
2. 2,3,4,5
3. 3,4,5,6
4. 1,2,4,5,
5. 1,3,5,6



Correct Answer : 1


Explanation: Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
The advantages of support vector machines are:
Effective in high dimensional spaces.
Still effective in cases where number of dimensions is greater than the number of samples.
Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.

The disadvantages of support vector machines include:
If the number of features is much greater than the number of samples, the method is likely to give poor performances.
SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation.




Question : Select the correct problems which can be solved using SVMs

1. SVMs are helpful in text and hypertext categorization
2. Classification of images can also be performed using SVMs
3. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
4. Hand-written characters can be recognized using SVM



1. 1,2 and 3
2. 2,3 and 4
3. 1,3, and 4
4. All 1,2,3 and 4 can be

Correct Answer : 4

SVMs can be used to solve various real world problems:
SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.
Hand-written characters can be recognized using SVM




Question : Support vector machines (SVMs) are a set of supervised learning methods used for

1. Linear classification
2. Non-linear classification
3. Regression
4. Only 1 and 3
5. All 1,2 and 3 are correct


Correct Answers: 5

Explanation: In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

Related Questions

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Question :

Classification and regression are examples of___________.

1. supervised learning
2. un-supervised learning
3. Clustering
4. Density estimation



Question : Reducing the data from many features to a small number so that we can
properly visualize it in two or three dimensions. It is done in_______

1. supervised learning
2. un-supervised learning
3. k-Nearest Neighbors
4. Support vector machines



Question : If you are trying to predict or forecast a discrete target value, then which is the correct options

1. Supervised Learning regression algorithms
2. Supervised Learning classification algorithms
3. Un supervised Learning
4. Density estimation algorithm


Question : Select the correct option from the below

1. If you're trying to predict or forecast a target value, then you need to look into supervised learning.
2. If you've chosen supervised learning, with discrete target value like Yes/No, 1/2/3, A/B/C, or Red/Yellow/Black, then look into classification.
3. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999, or +_ to -_, then you need to look unsupervised learning
4. If you're not trying to predict a target value, then you need to look into unsupervised learning
5. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering.


1. 1, 2,3,4,5
2. 2,3,4,5
3. 1,2,4,5
4. 1,2,3,5
5. 2,3,4,5


Question : Select the sequence of the developing machine learning applications
A. Analyze the input data
B. Prepare the input data
C. Collect data
D. Train the algorithm
E. Test the algorithm
F. Use It



1. A,B,C,D,E,F
2. C,B,A,D,E,F
3. C,A,B,D,E,F
4. C,B,A,D,E,F


Question :

Select the correct statement which applies to K-Nearest Neighbors

1. No Assumption about the data
2. Computationaly expensive
3. Require less memory
4. Works with Numeric Values


1. 1,2,3,4
2. 2,3,4
3. 1,3,4
4. 1,2,4